High-speed all-optical speckle decryption empowered by a physics-informed diffractive neural network

Speckle-based optical cryptosystems are promising technologies for information security. However, existing techniques mostly rely on digital decryption, resulting in computational delay and undermining the high-speed advantage of optical encryption. Moreover, conventional neural networks are typically effective only on images from the same distribution as the training datasets, limiting their general applicability. In this paper, we propose an all-optical high-speed decryption scheme for real-time recovery of speckle-encoded ciphertexts. By constructing a physics-informed diffractive neural network that approximates the inverse transmission matrix of the scattering medium, secret images can be directly reconstructed from speckle fields without optoelectronic conversion or post-processing. The network is trained with only 2048 samples from the MNIST dataset. Its transfer learning capability is validated across three out-of-distribution datasets, with decrypted images achieving a Pearson correlation coefficient of 0.82 and a structural similarity index measure of 0.75, demonstrating excellent transfer learning capability. For the first time, to our knowledge, this scheme simultaneously overcomes the bottlenecks of decryption delay and limited network generalizability in conventional speckle-based cryptosystems, achieving real-time image decryption with strong transferability. It provides a new pathway for developing low-power, real-time, and broadly applicable optical encryption systems, demonstrating significant potential for applications in high-speed security optical communications.